Pages that link to "Item:Q1715419"
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The following pages link to Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics (Q1715419):
Displaying 18 items.
- Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods (Q723115) (← links)
- Unraveling reservoir compaction parameters through the inversion of surface subsidence observations (Q732182) (← links)
- Simultaneous estimation of geologic and reservoir state variables within an ensemble-based multiple-point statistic framework (Q887566) (← links)
- Sequential data assimilation with multiple nonlinear models and applications to subsurface flow (Q1691889) (← links)
- Investigation of the sampling performance of ensemble-based methods with a simple reservoir model (Q1705872) (← links)
- Conditioning reservoir models on rate data using ensemble smoothers (Q1715362) (← links)
- Geophysics-based fluid-facies predictions using ensemble updating of binary state vectors (Q2040682) (← links)
- Resource and grade control model updating for underground mining production settings (Q2040720) (← links)
- Ensemble-based seismic and production data assimilation using selection Kalman model (Q2066808) (← links)
- Contaminant spill in a sandbox with non-Gaussian conductivities: simultaneous identification by the restart normal-score ensemble Kalman filter (Q2066823) (← links)
- A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping (Q2066842) (← links)
- Data-space inversion with ensemble smoother (Q2192795) (← links)
- A novel approach for subsurface characterization of coupled fluid flow and geomechanical deformation: the case of slightly compressible flows (Q2192844) (← links)
- Resource model updating for compositional geometallurgical variables (Q2238081) (← links)
- A rapid updating method to predict grade heterogeneity at smaller scales (Q2238112) (← links)
- A novel methodological approach for land subsidence prediction through data assimilation techniques (Q2240960) (← links)
- Error models for reducing history match bias (Q2508535) (← links)
- Randomized tensor decomposition for large-scale data assimilation problems for carbon dioxide sequestration (Q6084299) (← links)
